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Max Planck Institute for Multidisciplinary Sciences, Göttingen | Gottingen, Niedersachsen | Germany | 25 days ago
Job Code: 09-25 Job Offer from July 04, 2025 The Max Planck Institute for Multidisciplinary Sciences is a leading international research institute of exceptional scientific breadth. With more than
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, predict, and treat diseases. You will work with multimodal biomedical datasets including omics, imaging, and patient data and apply cutting-edge AI models such as graph neural networks, transformer
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to emerging carbon dioxide removal techniques. To this end, distributed pelagic imaging techniques enable the sustained observation of aquatic life and its debris, comprehensively covering the earth’s water
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Max Planck Institute for Multidisciplinary Sciences, Göttingen | Gottingen, Niedersachsen | Germany | 28 days ago
Job Code: 10-25 Job Offer from July 04, 2025 The Max Planck Institute for Multidisciplinary Sciences is a leading international research institute of exceptional scientific breadth. With more than
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the project include mathematical derivation, analysis, and comparison of models, methods, and simulation approaches; rapid prototyping of new ideas in custom code; implementation of new models, methods, and
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methods (LBM). For fluid simulations, we utilize the high-performance LBM framework waLBerla, predominantly written in C++, but increasingly adapted for GPU computations through automatic code generation
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static and dynamic 3D reconstruction, semantic scene understanding, and generative models for photo-realistic image / video synthesis. Overall, the main focus is on high-impact research with the aim
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., for convergence, existence, and uniqueness of solutions Fast prototyping of new ideas in individual code Implementation of new models, methods, and algorithms into an existing framework, with a focus on efficiency
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with such environments. We investigate machine learning approaches to infer semantic understanding of real-world scenes and the objects inside them from visual data, including images and depth/3D
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with such environments. We investigate machine learning approaches to infer semantic understanding of real-world scenes and the objects inside them from visual data, including images and depth/3D